The Experts below are selected from a list of 360 Experts worldwide ranked by ideXlab platform
Sohrab Saeb - One of the best experts on this subject based on the ideXlab platform.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:BACKGROUND Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. METHODS We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. RESULTS The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values < .05). We also found that these relationships were stronger when GPS features were calculated from weekend, compared to weekday, data. Although the correlation between baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features. DISCUSSION Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:Background Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. Methods We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. Results The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values Discussion Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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mobile phone sensor correlates of Depressive Symptom severity in daily life behavior an exploratory study
Journal of Medical Internet Research, 2015Co-Authors: Sohrab Saeb, Stephen M Schueller, Konrad P Kording, Mi Zhang, Christopher J Karr, Marya E Corden, David C MohrAbstract:Background: Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of Depressive Symptoms. Objective: The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying Depressive Symptom severity. Methods: A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results: A number of features from GPS data were related to Depressive Symptom severity, including circadian movement (regularity in 24-hour rhythm; r =-.63, P =.005), normalized entropy (mobility between favorite locations; r =-.58, P =.012), and location variance (GPS mobility independent of location; r =-.58, P =.012). Phone usage features, usage duration, and usage frequency were also correlated ( r =.54, P =.011, and r =.52, P =.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with Depressive Symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Conclusions: Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to Depressive Symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical Symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach. [J Med Internet Res 2015;17(7):e175]
David C Mohr - One of the best experts on this subject based on the ideXlab platform.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:BACKGROUND Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. METHODS We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. RESULTS The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values < .05). We also found that these relationships were stronger when GPS features were calculated from weekend, compared to weekday, data. Although the correlation between baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features. DISCUSSION Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:Background Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. Methods We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. Results The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values Discussion Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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mobile phone sensor correlates of Depressive Symptom severity in daily life behavior an exploratory study
Journal of Medical Internet Research, 2015Co-Authors: Sohrab Saeb, Stephen M Schueller, Konrad P Kording, Mi Zhang, Christopher J Karr, Marya E Corden, David C MohrAbstract:Background: Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of Depressive Symptoms. Objective: The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying Depressive Symptom severity. Methods: A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results: A number of features from GPS data were related to Depressive Symptom severity, including circadian movement (regularity in 24-hour rhythm; r =-.63, P =.005), normalized entropy (mobility between favorite locations; r =-.58, P =.012), and location variance (GPS mobility independent of location; r =-.58, P =.012). Phone usage features, usage duration, and usage frequency were also correlated ( r =.54, P =.011, and r =.52, P =.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with Depressive Symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Conclusions: Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to Depressive Symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical Symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach. [J Med Internet Res 2015;17(7):e175]
Faith Matcham - One of the best experts on this subject based on the ideXlab platform.
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predicting Depressive Symptom severity through individuals nearby bluetooth device count data collected by mobile phones preliminary longitudinal study
Jmir mhealth and uhealth, 2021Co-Authors: Yuezhou Zhang, Amos Folarin, Shaoxiong Sun, Nicholas Cummins, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Faith MatchamAbstract:Background: Research in mental health has found associations between depression and individuals’ behaviors and statuses, such as social connections and interactions, working status, mobility, and social isolation and loneliness. These behaviors and statuses can be approximated by the nearby Bluetooth device count (NBDC) detected by Bluetooth sensors in mobile phones. Objective: This study aimed to explore the value of the NBDC data in predicting Depressive Symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). Methods: The data used in this paper included 2886 biweekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the United Kingdom as part of the EU Remote Assessment of Disease and Relapse-Central Nervous System (RADAR-CNS) study. From the NBDC data 2 weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring the periodicity and regularity of individuals’ life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. Results: A number of significant associations were found between Bluetooth features and Depressive Symptom severity. Generally speaking, along with Depressive Symptom worsening, one or more of the following changes were found in the preceding 2 weeks of the NBDC data: (1) the amount decreased, (2) the variance decreased, (3) the periodicity (especially the circadian rhythm) decreased, and (4) the NBDC sequence became more irregular. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics (R2=0.526) and a root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE=4.547). Conclusions: Our statistical results indicate that the NBDC data have the potential to reflect changes in individuals’ behaviors and statuses concurrent with the changes in the Depressive state. The prediction results demonstrate that the NBDC data have a significant value in predicting Depressive Symptom severity. These findings may have utility for the mental health monitoring practice in real-world settings.
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predicting Depressive Symptom severity through individuals nearby bluetooth devices count data collected by mobile phones a preliminary longitudinal study
arXiv: Machine Learning, 2021Co-Authors: Yuezhou Zhang, Amos Folarin, Shaoxiong Sun, Nicholas Cummins, Yatharth Ranjan, Zulqarnain Rashid, Pauline Conde, Callum Stewart, Petroula Laiou, Faith MatchamAbstract:The Bluetooth sensor embedded in mobile phones provides an unobtrusive, continuous, and cost-efficient means to capture individuals' proximity information, such as the nearby Bluetooth devices count (NBDC). The continuous NBDC data can partially reflect individuals' behaviors and status, such as social connections and interactions, working status, mobility, and social isolation and loneliness, which were found to be significantly associated with depression by previous survey-based studies. This paper aims to explore the NBDC data's value in predicting Depressive Symptom severity as measured via the 8-item Patient Health Questionnaire (PHQ-8). The data used in this paper included 2,886 bi-weekly PHQ-8 records collected from 316 participants recruited from three study sites in the Netherlands, Spain, and the UK as part of the EU RADAR-CNS study. From the NBDC data two weeks prior to each PHQ-8 score, we extracted 49 Bluetooth features, including statistical features and nonlinear features for measuring periodicity and regularity of individuals' life rhythms. Linear mixed-effect models were used to explore associations between Bluetooth features and the PHQ-8 score. We then applied hierarchical Bayesian linear regression models to predict the PHQ-8 score from the extracted Bluetooth features. A number of significant associations were found between Bluetooth features and Depressive Symptom severity. Compared with commonly used machine learning models, the proposed hierarchical Bayesian linear regression model achieved the best prediction metrics, R2= 0.526, and root mean squared error (RMSE) of 3.891. Bluetooth features can explain an extra 18.8% of the variance in the PHQ-8 score relative to the baseline model without Bluetooth features (R2=0.338, RMSE = 4.547).
Stephen M Schueller - One of the best experts on this subject based on the ideXlab platform.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:BACKGROUND Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. METHODS We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. RESULTS The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values < .05). We also found that these relationships were stronger when GPS features were calculated from weekend, compared to weekday, data. Although the correlation between baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features. DISCUSSION Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:Background Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. Methods We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. Results The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values Discussion Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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mobile phone sensor correlates of Depressive Symptom severity in daily life behavior an exploratory study
Journal of Medical Internet Research, 2015Co-Authors: Sohrab Saeb, Stephen M Schueller, Konrad P Kording, Mi Zhang, Christopher J Karr, Marya E Corden, David C MohrAbstract:Background: Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of Depressive Symptoms. Objective: The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying Depressive Symptom severity. Methods: A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results: A number of features from GPS data were related to Depressive Symptom severity, including circadian movement (regularity in 24-hour rhythm; r =-.63, P =.005), normalized entropy (mobility between favorite locations; r =-.58, P =.012), and location variance (GPS mobility independent of location; r =-.58, P =.012). Phone usage features, usage duration, and usage frequency were also correlated ( r =.54, P =.011, and r =.52, P =.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with Depressive Symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Conclusions: Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to Depressive Symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical Symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach. [J Med Internet Res 2015;17(7):e175]
Konrad P Kording - One of the best experts on this subject based on the ideXlab platform.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:BACKGROUND Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. METHODS We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. RESULTS The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values < .05). We also found that these relationships were stronger when GPS features were calculated from weekend, compared to weekday, data. Although the correlation between baseline PHQ-9 scores with 2-week GPS features diminished as we moved further from baseline, correlations with the end-of-study scores remained significant regardless of the time point used to calculate the features. DISCUSSION Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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the relationship between mobile phone location sensor data and Depressive Symptom severity
PeerJ, 2016Co-Authors: Sohrab Saeb, Emily G Lattie, Stephen M Schueller, Konrad P Kording, David C MohrAbstract:Background Smartphones offer the hope that depression can be detected using passively collected data from the phone sensors. The aim of this study was to replicate and extend previous work using geographic location (GPS) sensors to identify Depressive Symptom severity. Methods We used a dataset collected from 48 college students over a 10-week period, which included GPS phone sensor data and the Patient Health Questionnaire 9-item (PHQ-9) to evaluate Depressive Symptom severity at baseline and end-of-study. GPS features were calculated over the entire study, for weekdays and weekends, and in 2-week blocks. Results The results of this study replicated our previous findings that a number of GPS features, including location variance, entropy, and circadian movement, were significantly correlated with PHQ-9 scores (r's ranging from -0.43 to -0.46, p-values Discussion Our findings were consistent with past research demonstrating that GPS features may be an important and reliable predictor of Depressive Symptom severity. The varying strength of these relationships on weekends and weekdays suggests the role of weekend/weekday as a moderating variable. The finding that GPS features predict Depressive Symptom severity up to 10 weeks prior to assessment suggests that GPS features may have the potential as early warning signals of depression.
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mobile phone sensor correlates of Depressive Symptom severity in daily life behavior an exploratory study
Journal of Medical Internet Research, 2015Co-Authors: Sohrab Saeb, Stephen M Schueller, Konrad P Kording, Mi Zhang, Christopher J Karr, Marya E Corden, David C MohrAbstract:Background: Depression is a common, burdensome, often recurring mental health disorder that frequently goes undetected and untreated. Mobile phones are ubiquitous and have an increasingly large complement of sensors that can potentially be useful in monitoring behavioral patterns that might be indicative of Depressive Symptoms. Objective: The objective of this study was to explore the detection of daily-life behavioral markers using mobile phone global positioning systems (GPS) and usage sensors, and their use in identifying Depressive Symptom severity. Methods: A total of 40 adult participants were recruited from the general community to carry a mobile phone with a sensor data acquisition app (Purple Robot) for 2 weeks. Of these participants, 28 had sufficient sensor data received to conduct analysis. At the beginning of the 2-week period, participants completed a self-reported depression survey (PHQ-9). Behavioral features were developed and extracted from GPS location and phone usage data. Results: A number of features from GPS data were related to Depressive Symptom severity, including circadian movement (regularity in 24-hour rhythm; r =-.63, P =.005), normalized entropy (mobility between favorite locations; r =-.58, P =.012), and location variance (GPS mobility independent of location; r =-.58, P =.012). Phone usage features, usage duration, and usage frequency were also correlated ( r =.54, P =.011, and r =.52, P =.015, respectively). Using the normalized entropy feature and a classifier that distinguished participants with Depressive Symptoms (PHQ-9 score ≥5) from those without (PHQ-9 score <5), we achieved an accuracy of 86.5%. Furthermore, a regression model that used the same feature to estimate the participants’ PHQ-9 scores obtained an average error of 23.5%. Conclusions: Features extracted from mobile phone sensor data, including GPS and phone usage, provided behavioral markers that were strongly related to Depressive Symptom severity. While these findings must be replicated in a larger study among participants with confirmed clinical Symptoms, they suggest that phone sensors offer numerous clinical opportunities, including continuous monitoring of at-risk populations with little patient burden and interventions that can provide just-in-time outreach. [J Med Internet Res 2015;17(7):e175]